Screenshotting What’s Important in Video Data: An Experiment in Collaborative, Subjective Analysis of Artifactual, Cultural Research with Children
Bibliographic record
Abstract
When using video and visual methods in qualitative and post-qualitative research, the size and scale of the data set can be overwhelming, particularly for new researchers. Collaborative research teams often work with a code book to systematize and unify their analyses. Interpretive researchers pursuing multi-layered and multi-voiced visual analysis often find it difficult to move away from desires for a single ‘best’ interpretation of what happened. This paper illustrates and interrogates an open and flexible method for ‘thinning’ (screenshotting) video data that we call the ‘Five Images Method’. We offer one unfolding of interpretive processes and tensions and examine how four researchers worked across positionalities to analyse video data. We start with our positionalities in relation to a research study of children creating photographic and written stories of cultural artifacts, carried out over one year. The primary data from the study was generated through online video-conference sessions connecting a university researcher with an elementary class. A second level of data was created through a process of screenshotting, followed by recursive cycles of conversation about the choices of each researcher, and how they were guided by background, geography, roles in relation to child participants, technologies, personal experiences, and so on. Two key incidents that illustrate the potential of the method and the interpretations produced are described. We argue that reducing video data in this way can be both generative and limiting, while also serving as a catalyst for enhanced analysis. The collaborations and relationships built in research teams through slow processes of analysis (and writing!) working across difference also promote evocative and layered learning. Looking at interpretations as multiple can be hampered by longstanding histories of research as intended to produce authentic and singular truths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".